Users of an environment, be it enclosed buildings, open spaces, or urban and rural roads, dynamically generate patterns of movement as the users move around in the environment. Users can be people, vehicles, or other mobile objects.
However, most automated systems for such environments, such as heating, cooling, lighting, elevator, security, traffic control systems, do not consider patterns of movement to dynamically adjust their operation for the users, e.g., building occupants, vehicles, or other mobile objects.
At most, elevator systems may have a pre-programmed schedule that favors up-traffic in mornings, and down-traffic in the late afternoons. Similarly, HVAC systems may have different pre-programmed day-time and night-time operational settings. Traffic lights can also be preprogrammed. There are some devices, such as automated appliances that include sensors that respond to local movement. However, most systems are generally insensitive to large scale patterns of movement in the environment.
It is desired to place a sensor network in an environment so that patterns of movement, i.e. traffic flow, of users in the environment can be determined. In addition, it is desired to predict future activities of the users based on known patterns of users.
Sensor networks, static and ad-hoc, are well known. It is preferred to use an ad-hoc sensor network. This makes it easy to relocate sensors as configurations of the environment, and patterns of usage change over time. Thus, the sensors can be adapted to current or future patterns of usage.
However, in either case, to make data acquired by the sensors useful for location specific analysis, it is necessary to determine a geometry of the sensors with respect to the environment. The geometry defines the spatial relationship between the sensors. It is desired to do this automatically and passively with just the sensors themselves.
Nissanka, et al., “The cricket location-support system,” Proc. of the Sixth Annual ACM International Conference on Mobile Computing and Networking, August 2000, describe a sensor network that times ultrasonic signals to determine locations of sensors. That is an active system that uses specialized components and processing. Other similar techniques based on RF signals are described by LaMarcal, et al., “Plantcare: An investigation in practical ubiquitous systems,” Fourth International Conference on Ubiquitous Computing, 2002, and Sahinoglu, “Location Estimation in Partially Synchronized Networks, U.S. patent application Ser. No. 09/649,759, filed on Aug. 26, 2003. Those systems are relatively complex. For many applications, the resolution of the geometry of the sensors in the network does not warrant the cost and complexity involved with the prior art solutions.
Tracking data have been used in the prior art to determine patterns of movement, see W. E. L. Grimson, et al., “Using adaptive tracking to classify and monitor activities in a site,” IEEE CVPR, June 1998, and Johnson, et al. “Learning the distribution of object trajectories for event recognition,” Image and Vision Computing, 14(8), 1996. Those methods require the tracking and identification of specific objects in an environment over time.
The Aware Home project at Georgia Institute of Technology follows a similar idiom of attempting to understand behavior from relatively low-fidelity models, see Kidd, et al., “The aware home: A living laboratory for ubiquitous computing research,” Proceedings of Second International Workshop on Cooperative Buildings, October 1999. That work also requires tracking data of particular individual objects or users in order to determine pattern information in the environment.
In the prior art, event prediction in an environment has also required tracking data for particular objects, see U.S. Pat. No. 6,587,781, issued to Feldman, et al., on Jul. 1, 2003. That method requires voluminous traffic data acquired from a variety of sources be prioritized, filtered and controlled before any processing step can be applied to the data, and a geometry of the environment must be known.
It is desired to model traffic flow with sensor networks. It is desired to do this passively and without having to identify events with specific objects. It is also desired to predict future activities based on the traffic flow. Furthermore, it is desired to determine geometries of sensor networks in a similar manner.